Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	17,667
2	New Jersey	15,845
3	Massachusetts	11,509
4	Rhode Island	10,963
5	Connecticut	9,629
6	District of Columbia	9,188
7	Delaware	6,922
8	Louisiana	6,894
9	Illinois	6,563
10	Maryland	5,652
11	Michigan	4,800
12	Pennsylvania	4,796
13	Nebraska	4,515
14	South Dakota	4,140
15	Iowa	4,092
16	Indiana	3,813
17	Colorado	3,490
18	Mississippi	3,329
19	Georgia	3,137
20	Virginia	3,022
21	Kansas	2,537
22	New Mexico	2,485
23	Washington	2,429
24	New Hampshire	2,382
25	Tennessee	2,310
26	Minnesota	2,215
27	Ohio	2,160
28	Alabama	2,134
29	North Dakota	2,061
30	Nevada	2,048
31	Utah	2,013
32	Florida	1,951
33	Wisconsin	1,823
34	California	1,800
35	Missouri	1,649
36	Arizona	1,612
37	Kentucky	1,567
38	South Carolina	1,539
39	Vermont	1,485
40	North Carolina	1,468
41	Texas	1,460
42	Arkansas	1,379
43	Idaho	1,283
44	Oklahoma	1,195
45	Wyoming	1,166
46	Maine	1,098
47	Oregon	796
48	West Virginia	768
49	Puerto Rico	719
50	Alaska	523
51	Hawaii	440
52	Montana	431

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	196
2	Illinois	188
3	District of Columbia	180
4	Delaware	158
5	Maryland	139
6	New Jersey	137
7	Iowa	131
8	Connecticut	126
9	Massachusetts	125
10	South Dakota	101
11	Virginia	101
12	Minnesota	100
13	New York	88
14	Nebraska	86
15	Indiana	71
16	Pennsylvania	71
17	New Mexico	68
18	Mississippi	59
19	Georgia	57
20	New Hampshire	55
21	Alabama	54
22	Kansas	53
23	Tennessee	53
24	Colorado	48
25	Arkansas	46
26	North Dakota	46
27	Louisiana	45
28	Ohio	44
29	Michigan	40
30	Texas	40
31	Kentucky	39
32	Wisconsin	38
33	California	36
34	Utah	36
35	Arizona	35
36	North Carolina	32
37	Washington	32
38	Florida	29
39	Nevada	25
40	South Carolina	25
41	Missouri	21
42	Oklahoma	20
43	Maine	17
44	Oregon	15
45	Puerto Rico	13
46	Wyoming	12
47	Idaho	11
48	West Virginia	5
49	Vermont	3
50	Alaska	2
51	Hawaii	0
52	Montana	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,402
2	New Jersey	1,070
3	Connecticut	852
4	Massachusetts	745
5	Louisiana	490
6	District of Columbia	476
7	Michigan	468
8	Rhode Island	419
9	Pennsylvania	306
10	Maryland	290
11	Illinois	285
12	Delaware	243
13	Indiana	234
14	Colorado	175
15	Mississippi	153
16	Georgia	138
17	Washington	127
18	Ohio	122
19	Minnesota	108
20	Nevada	104
21	New Hampshire	104
22	New Mexico	104
23	Virginia	104
24	Iowa	91
25	Alabama	88
26	Missouri	86
27	Vermont	86
28	Florida	82
29	Arizona	77
30	Kentucky	74
31	California	73
32	Wisconsin	71
33	Oklahoma	70
34	South Carolina	68
35	Kansas	64
36	North Carolina	56
37	Nebraska	53
38	North Dakota	49
39	Maine	48
40	South Dakota	44
41	Texas	40
42	Idaho	38
43	Tennessee	38
44	Puerto Rico	35
45	West Virginia	32
46	Arkansas	31
47	Oregon	30
48	Utah	22
49	Montana	14
50	Hawaii	12
51	Wyoming	12
52	Alaska	10

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Massachusetts	14
2	New Jersey	14
3	District of Columbia	11
4	New York	11
5	Connecticut	10
6	Rhode Island	8
7	Maryland	7
8	Illinois	6
9	Louisiana	6
10	Delaware	5
11	Indiana	4
12	Michigan	4
13	Mississippi	4
14	New Mexico	4
15	Alabama	3
16	Iowa	3
17	Minnesota	3
18	Pennsylvania	3
19	Colorado	2
20	Georgia	2
21	Missouri	2
22	New Hampshire	2
23	Ohio	2
24	Virginia	2
25	Washington	2
26	Arizona	1
27	California	1
28	Kansas	1
29	Kentucky	1
30	Nebraska	1
31	Nevada	1
32	North Carolina	1
33	North Dakota	1
34	South Carolina	1
35	South Dakota	1
36	Tennessee	1
37	Wisconsin	1
38	Alaska	0
39	Arkansas	0
40	Florida	0
41	Hawaii	0
42	Idaho	0
43	Maine	0
44	Montana	0
45	Oklahoma	0
46	Oregon	0
47	Puerto Rico	0
48	Texas	0
49	Utah	0
50	Vermont	0
51	West Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	120,879	1	99
Dakota	Nebraska	72,556	2	99
Lincoln	Arkansas	72,328	3	99
Nobles	Minnesota	59,688	4	99
Cass	Indiana	40,967	5	99
Davidson	Tennessee	5,453	241	92
Richland	South Carolina	2,754	575	81
Pierce	Washington	1,913	811	74
Orange	California	1,165	1197	61
York	South Carolina	918	1424	54

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Randolph	Georgia	3,098	1	99
Terrell	Georgia	2,813	2	99
Early	Georgia	2,650	3	99
Chautauqua	Kansas	2,462	4	99
Nassau	New York	1,808	5	99
Richland	South Carolina	142	468	85
Pierce	Washington	70	788	74
Davidson	Tennessee	53	938	70
Orange	California	25	1273	59
York	South Carolina	18	1386	55

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.